# Copyright (c) Facebook, Inc. and its affiliates.
# Copyright 2020 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# --------------------------------------------------------
# Copyright (c) OpenMMLab. All rights reserved.
from .builder import DATASETS
from .custom import CustomDataset


@DATASETS.register_module()
class COCOStuffDataset(CustomDataset):
    """COCO-Stuff dataset.

    In segmentation map annotation for COCO-Stuff, Train-IDs of the 10k version
    are from 1 to 171, where 0 is the ignore index, and Train-ID of COCO Stuff
    164k is from 0 to 170, where 255 is the ignore index. So, they are all 171
    semantic categories. ``reduce_zero_label`` is set to True and False for the
    10k and 164k versions, respectively. The ``img_suffix`` is fixed to '.jpg',
    and ``seg_map_suffix`` is fixed to '.png'.
    """
    CLASSES = (
        'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
        'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
        'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep',
        'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella',
        'handbag', 'tie', 'suitcase', 'frisbee', 'skis', 'snowboard',
        'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard',
        'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup', 'fork',
        'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich', 'orange',
        'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair',
        'couch', 'potted plant', 'bed', 'dining table', 'toilet', 'tv',
        'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave',
        'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase',
        'scissors', 'teddy bear', 'hair drier', 'toothbrush', 'banner',
        'blanket', 'branch', 'bridge', 'building-other', 'bush', 'cabinet',
        'cage', 'cardboard', 'carpet', 'ceiling-other', 'ceiling-tile',
        'cloth', 'clothes', 'clouds', 'counter', 'cupboard', 'curtain',
        'desk-stuff', 'dirt', 'door-stuff', 'fence', 'floor-marble',
        'floor-other', 'floor-stone', 'floor-tile', 'floor-wood',
        'flower', 'fog', 'food-other', 'fruit', 'furniture-other', 'grass',
        'gravel', 'ground-other', 'hill', 'house', 'leaves', 'light', 'mat',
        'metal', 'mirror-stuff', 'moss', 'mountain', 'mud', 'napkin', 'net',
        'paper', 'pavement', 'pillow', 'plant-other', 'plastic', 'platform',
        'playingfield', 'railing', 'railroad', 'river', 'road', 'rock', 'roof',
        'rug', 'salad', 'sand', 'sea', 'shelf', 'sky-other', 'skyscraper',
        'snow', 'solid-other', 'stairs', 'stone', 'straw', 'structural-other',
        'table', 'tent', 'textile-other', 'towel', 'tree', 'vegetable',
        'wall-brick', 'wall-concrete', 'wall-other', 'wall-panel',
        'wall-stone', 'wall-tile', 'wall-wood', 'water-other', 'waterdrops',
        'window-blind', 'window-other', 'wood')

    PALETTE = [[0, 192, 64], [0, 192, 64], [0, 64, 96], [128, 192, 192],
               [0, 64, 64], [0, 192, 224], [0, 192, 192], [128, 192, 64],
               [0, 192, 96], [128, 192, 64], [128, 32, 192], [0, 0, 224],
               [0, 0, 64], [0, 160, 192], [128, 0, 96], [128, 0, 192],
               [0, 32, 192], [128, 128, 224], [0, 0, 192], [128, 160, 192],
               [128, 128, 0], [128, 0, 32], [128, 32, 0], [128, 0, 128],
               [64, 128, 32], [0, 160, 0], [0, 0, 0], [192, 128, 160],
               [0, 32, 0], [0, 128, 128], [64, 128, 160], [128, 160, 0],
               [0, 128, 0], [192, 128, 32], [128, 96, 128], [0, 0, 128],
               [64, 0, 32], [0, 224, 128], [128, 0, 0], [192, 0, 160],
               [0, 96, 128], [128, 128, 128], [64, 0, 160], [128, 224, 128],
               [128, 128, 64], [192, 0, 32], [128, 96, 0], [128, 0, 192],
               [0, 128, 32], [64, 224, 0], [0, 0, 64], [128, 128, 160],
               [64, 96, 0], [0, 128, 192], [0, 128, 160], [192, 224, 0],
               [0, 128, 64], [128, 128, 32], [192, 32, 128], [0, 64, 192],
               [0, 0, 32], [64, 160, 128], [128, 64, 64], [128, 0, 160],
               [64, 32, 128], [128, 192, 192], [0, 0, 160], [192, 160, 128],
               [128, 192, 0], [128, 0, 96], [192, 32, 0], [128, 64, 128],
               [64, 128, 96], [64, 160, 0], [0, 64, 0], [192, 128, 224],
               [64, 32, 0], [0, 192, 128], [64, 128, 224], [192, 160, 0],
               [0, 192, 0], [192, 128, 96], [192, 96, 128], [0, 64, 128],
               [64, 0, 96], [64, 224, 128], [128, 64, 0], [192, 0, 224],
               [64, 96, 128], [128, 192, 128], [64, 0, 224], [192, 224, 128],
               [128, 192, 64], [192, 0, 96], [192, 96, 0], [128, 64, 192],
               [0, 128, 96], [0, 224, 0], [64, 64, 64], [128, 128, 224],
               [0, 96, 0], [64, 192, 192], [0, 128, 224], [128, 224, 0],
               [64, 192, 64], [128, 128, 96], [128, 32, 128], [64, 0, 192],
               [0, 64, 96], [0, 160, 128], [192, 0, 64], [128, 64, 224],
               [0, 32, 128], [192, 128, 192], [0, 64, 224], [128, 160, 128],
               [192, 128, 0], [128, 64, 32], [128, 32, 64], [192, 0, 128],
               [64, 192, 32], [0, 160, 64], [64, 0, 0], [192, 192, 160],
               [0, 32, 64], [64, 128, 128], [64, 192, 160], [128, 160, 64],
               [64, 128, 0], [192, 192, 32], [128, 96, 192], [64, 0, 128],
               [64, 64, 32], [0, 224, 192], [192, 0, 0], [192, 64, 160],
               [0, 96, 192], [192, 128, 128], [64, 64, 160], [128, 224, 192],
               [192, 128, 64], [192, 64, 32], [128, 96, 64], [192, 0, 192],
               [0, 192, 32], [64, 224, 64], [64, 0, 64], [128, 192, 160],
               [64, 96, 64], [64, 128, 192], [0, 192, 160], [192, 224, 64],
               [64, 128, 64], [128, 192, 32], [192, 32, 192], [64, 64, 192],
               [0, 64, 32], [64, 160, 192], [192, 64, 64], [128, 64, 160],
               [64, 32, 192], [192, 192, 192], [0, 64, 160], [192, 160, 192],
               [192, 192, 0], [128, 64, 96], [192, 32, 64], [192, 64, 128],
               [64, 192, 96], [64, 160, 64], [64, 64, 0]]

    def __init__(self, **kwargs):
        super(COCOStuffDataset, self).__init__(
            img_suffix='.jpg', seg_map_suffix='_labelTrainIds.png', **kwargs)
